An improved algorithm (SSA-BP) based on sparrow search algorithm (SSA) to optimize BP (back propagation) neural network was proposed for 4D track prediction, which improved the accuracy of track prediction by neural network alone to a certain extent. Firstly, the sparrow algorithm was used to find the global optimal solution, improve the quality of the initial solution, and enhance the global search ability of the algorithm. Secondly, the optimal solution was used as the input layer of BP neural network, and the weights and thresholds were updated to optimize the self-learning and adaptive ability of BP neural network. Then, the 4D trajectory of the aircraft was formed by interpolating ADS-B(automatic dependent surveillance-broadcast,) data and used as input variables for the SSA-BP model's 4D trajectory prediction using the sparrow algorithm. Finally, the prediction results of the SSA-BP algorithm model and the simple BP network model were compared with the real trajectory of the aircraft. Experiments show that in the specified time series, the three errors of longitude, latitude and height of SSA-BP algorithm model are smaller than those of simple BP neural network model, and can achieve high-precision 4D track prediction.